Spatial modeling is a core element in geographical information science. It incorporates geographic information to construct the relationship for interpreting the behavior of spatial phenomena. In this paper, a broad learning framework for nonparametric spatial modeling is presented. Broad learning overcomes the obstacle of expensive computational consumption in deep learning and provides a powerful computationally efficient alternative. In contrast to the deep learning architecture that is configured with stacks of hierarchical layers, broad learning networks are established in a flat manner that can be flexibly reconfigured with the inherited information from the trained network. To develop the broad learning network, a simple prototype network is established as the initial trial and it is modified incrementally to enhance its data fitting capacity. Consequently, complex relationship of unstructured spatial data can be modeled efficiently. To demonstrate the efficacy and applicability of the broad learning framework, we will present a simulated example and a real application using the strong ground motion records on the 2008 great Wenchuan earthquake.

中文翻译：

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非参数空间建模的广泛学习及其在地震衰减中的应用

空间建模是地理信息科学的核心要素。它结合了地理信息以构建用于解释空间现象行为的关系。在本文中，提出了一种用于非参数空间建模的广泛学习框架。广泛的学习克服了深度学习中昂贵的计算消耗的障碍，并提供了强大的计算效率替代方案。与配置有分层层的堆栈的深度学习体系结构相反，广泛的学习网络以一种扁平的方式建立，可以用来自受训网络的继承信息灵活地重新配置。为了发展广泛的学习网络，建立了一个简单的原型网络作为初始试验，并对其进行了逐步修改以增强其数据拟合能力。因此，可以有效地建模非结构化空间数据的复杂关系。为了展示广泛学习框架的有效性和适用性，我们将使用强大的地面运动记录对2008年汶川大地震进行演示，并提供一个模拟示例和一个实际应用。